Recently, a lot of attention has been focused on the incorporation of 3D data
into face analysis and its applications. Despite providing a more accurate
representation of the face, 3D facial images are more complex to acquire than
2D pictures. As a consequence, great effort has been invested in developing
systems that reconstruct 3D faces from an uncalibrated 2D image. However, the
3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is
needed to restrict the solutions space. In this work, we review 3D face
reconstruction methods proposed in the last decade, focusing on those that only
use 2D pictures captured under uncontrolled conditions. We present a
classification of the proposed methods based on the technique used to add prior
knowledge, considering three main strategies, namely, statistical model
fitting, photometry, and deep learning, and reviewing each of them separately.
In addition, given the relevance of statistical 3D facial models as prior
knowledge, we explain the construction procedure and provide a list of the most
popular publicly available 3D facial models. After the exhaustive study of
3D-from-2D face reconstruction approaches, we observe that the deep learning
strategy is rapidly growing since the last few years, becoming the standard
choice in replacement of the widespread statistical model fitting. Unlike the
other two strategies, photometry-based methods have decreased in number due to
the need for strong underlying assumptions that limit the quality of their
reconstructions compared to statistical model fitting and deep learning
methods. The review also identifies current challenges and suggests avenues for
future research.

本文综述了过去十年中提出的三维人脸重建方法，重点关注了那些只使用在不受控制条件下捕获的二维图片的方法。分类基于用于添加先验知识的技术，考虑了统计模型拟合、光度计和深度学习这三种主要策略，并分别审查了每种策略。我们发现，深度学习策略正在迅速增长，取代了广泛的统计模型拟合策略。相比之下，光度学方法的数量较少，因为需要强烈的基本假设来限制质量。此外，本文还确定了当前的挑战，并提出了未来研究的方向。